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The way buyers find, evaluate, and buy products is undergoing the most significant transformation since eCommerce started. The familiar pattern of someone typing keywords into a search bar, then using filters and facets to page through results, is giving way to something fundamentally different: conversational, context-aware, and agent-driven commerce.

As buyers evolve, so too must eCommerce websites. But this transition is far less daunting than it appears. The path to agent-driven commerce begins with conversational AI search: a convergence of large language models, intelligent agents, and robust search infrastructure that together form the foundation of next-generation eCommerce experiences.

1. Large language models are capable of understanding complex, context-aware requirements, which can be used to provide answers to intent-based queries, like questions about a product or its application. This is a “ChatGPT-like” experience.
2. Enterprise-grade agents have the proper guardrails and rules, such as merchandising rules and entitlements, to drive fluid conversations, as well as the ability to perform actions like adding products to a cart, checking out, or reaching out to other systems of record, like order history, to tell customers what they’ve purchased in the past.
3. Traditional RAG (retrieval augmented generation) search infrastructure understands your product catalog, product attributes, and specifications, so that the right products are showing up in the first place.

The path forward is less disruptive than it sounds. A shopping assistant isn't a platform replacement or a new feature. It’s a paradigm shift.

The convergence of these three things—large language models, enterprise-grade AI agents, and search infrastructure—creates a new buying journey.

The shift to this new buying journey isn’t a future roadmap item anymore. It’s now.

The Shift from Keywords to Intent-Based Search

For two decades, search in commerce meant one thing: keywords. A consumer who wanted a white t-shirt, or a buyer who needed a stainless steel compression fitting, would type fragments of that requirement, like “white tee” or “compression fitting,” and hope the catalog surfaced something useful. The burden of translation from human intent to machine-readable query fell entirely on the buyer.

Natural Language, Intent-Based Search, Changed That

That model no longer works as buyers use websites that understand natural language and user intent. Buyers have grown accustomed to these conversational interfaces and to asking complex questions, expecting answers. They are bringing those expectations to work.

In a 2024 survey of enterprise procurement professionals, 72% reported that they now prefer to describe their requirements in natural language and expect the system to return precise, relevant results without requiring keyword refinement.

Modern AI-powered search engines, powered by LLMs and multimodal models, can understand a sentence like “need a non-sparking muffler for a YZ250” or “a recommendation for a power saw to help me build a shed in my backyard” and return a curated, ranked list of products.

In this new paradigm, that is not a complex question when the system has guardrails in place to use product specs, user buying history, and an LLM to provide real-world context.

Why This Matters to You

B2C, or consumer eCommerce, benefited from natural language search earlier because product catalogs are simpler and queries are shorter. B2B has been slower to adopt because products are more technical and decision processes take longer. However, in both cases, a shopping assistant now has access to more technology to handle:

• Catalogs containing thousands of SKUs with overlapping attributes
• Specifications that are detailed, technical, and context-dependent
• Recommendations where mistakes can lead to returns, customer dissatisfaction, or compliance failures

These complexities mean that companies adopting intent-based search create an enormous advantage. Buyers will return to brands that understand them without friction, and they will recommend them to friends and colleagues.

The Path to Conversational Agentic Commerce

The window for proactive investment is now. Organizations that push this down the priority list risk not having the infrastructure to compete effectively. The following outlines the pathway toward a website that contains all of these elements and ultimately offers agentic commerce.

Step 1: Develop an Agentic Commerce Roadmap

Define your organization’s target state for AI-ready commerce infrastructure and build a phased roadmap to reach it. This roadmap should have board-level visibility. Organizations leading in this space are treating it as an infrastructure investment equivalent to ERP or eCommerce platform decisions.

Step 2: Add Structured Product Data

Implement a plan to add data from other platforms, such as your ERP or PIM. This will give an AI-powered search platform the ability to access structured data that can be used in an agentic eCommerce experience, such as order history.

Step 3: Invest in Catalog Data Quality

Data quality is the foundation that all other capabilities depend on. An agent that queries a catalog with incomplete specifications, missing certifications, or inaccurate inventory data will return poor results. Catalog data quality is now a revenue protection imperative.

Step 4: Upgrade to Intent-Aware Search

Replace or augment legacy keyword search with a semantic search engine capable of understanding natural language queries, domain-specific vocabulary, and attribute-based filtering. This investment pays for itself in improved buyer experience while also preparing the infrastructure for agent-driven traffic.

• Evaluate AI-powered vector search platforms and perform test searches against your catalog
• Assess whether new search platforms can handle data gaps and inconsistencies in naming, descriptions, and attributes
• Implement query-level analytics to identify where buyers fail to find what they need

Step 5: Implement a Conversational Search Experience

Launch a conversational interface that can handle natural language queries, remember context within a session, and surface relevant products from complex specifications. Consider launching it internally for staff, externally for customers, or both.

This interface will serve two functions simultaneously: improving human buyer experience and providing the foundation for full agentic commerce.

Conclusion

The shift from keyword search to intent-based, agent-intermediated commerce is not a distant scenario. It’s happening now. The organizations that will define the market of 2027 and beyond are making infrastructure investments today.

The buyers driving that spending will disproportionately favor companies that meet them where they are: in natural language, with accurate data, at scale.

What Comes Next: The Rise of Agentic Commerce

You’ve seen how search evolves into conversation. Now, see how it becomes action. In Part II of this series, we explore how conversational commerce evolves into fully agentic commerce, where AI agents take action, complete purchases, and reshape the buying journey. Read Part II: From Conversations to Agents